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In recent decades, rise of sea surface temperature (SST) in the Kuroshio region of the East China Sea (ECS), which is associated with global warming, has attracted considerable attention. Despite its relevance to air‐sea interaction phenomena, the atmospheric consequences of this SST increase remain largely unexplored. Using the ERA5 reanalysis data set in conjunction with a moisture budget analysis, we found that during 1979–2022, warming in the ECS‐Kuroshio has contributed to the intensification of the East Asian Meiyu‐Baiu rainband in June. This intensification is attributed to augmented wind convergence in the low‐level free troposphere (950–700 hPa). Importantly, the atmospheric responses to ECS‐Kuroshio warming penetrated the deep troposphere (to approximately 300 hPa), suggesting an enhancement of deep convection. Furthermore, ECS‐Kuroshio warming likely strengthened the overlying atmospheric low pressure, resulting in wind convergence enhancement. The findings clarified the important role of the ECS‐Kuroshio in driving East Asian climate change amid global warming. Plain Language Summary In response to global warming, East Asian summer precipitation is increasing along with an increase in extreme precipitation events, which poses a significant threat to agriculture and economic development. Understanding the mechanisms underlying the increased precipitation trend in East Asia is crucial for sustainable human development in this region. The findings of this study revealed that East Asian precipitation induced by the Meiyu‐Baiu rainband has intensified continuously during the past four decades, particularly in the marine areas around the East China Sea (ECS) and the south of Japan. We further established that the continuous increase in sea surface temperature over the Kuroshio in the ECS, which was related to global warming, strengthened the atmospheric low‐pressure system overlying the current, contributing to an increase in rainfall around the ECS. The findings of this study highlighted the important role of the Kuroshio Current in influencing climate change in East Asia. Key Points The Meiyu‐Baiu rainband in June intensified over the marine areas around the Kuroshio and south of Japan during 1979–2022 The intensified Meiyu‐Baiu rainband was caused mainly by the wind convergence accompanied by enhanced deep convection Kuroshio warming in the East China Sea likely intensified the Meiyu‐Baiu rainband by a pressure adjustment mechanism

Tropical cyclone (TC)‐induced rainfall is a drastic threat to human life and property, and thus rational rainstorm nowcasts within even a lead time of few hours play a key role in disaster mitigation. While recent deep learning‐based algorithms have shown promise, predictions commonly suffer from the troubles of blur, dissipation, and location errors of TC rainbands, particularly for a lead time beyond 1 hr. Here, we develop a new nowcasting model, named TyrainNow, and show a significant improvement for nowcasting rainbands with a lead time up to 2 hr. Concretely, TyrainNow employs a refined multi‐task loss function integrating geographical consistency, temporal coherence and radar image structural similarity. This tailored enhancement is architecture‐agnostic and involves subtle adjustments. Secondly, TyrainNow adopts the quantile mapping technique to correct systematic attenuation biases inherent in the neural network outputs. The new model is verified on the basis of typhoon radar composite reflectivity products in South China, with a focus on the Greater Bay Area. Specifically, the new model achieves a critical success index of 0.099 at the 40 dBZ threshold, marking a substantial improvement from 27% to 330% compared to three other benchmark models, DGMR (0.070), PredRNN‐v2 (0.023), and optical flow model (0.078), averaged over the lead times between 1 and 2 hr. We further verify the model's explainability and generalizability, and recommend it as a scalable and reliable model.

Rainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific. Plain Language Summary Rainbands are a salient feature of tropical cyclones (TCs) and are closely related to TC structure and intensity change. Synthetic aperture radar (SAR) can capture the sea surface imprint of rainbands beneath clouds caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we made 464 rainband‐annotated data. The data were mapped to grid nodes spaced at 0.027 times the radius of max winds in a coordinate system with the origin at the TC center and the y‐axis in the vertical wind shear direction. Then, the data were composited to estimate and further investigate the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwestern Pacific than in the North Atlantic and Northeast Pacific. Key Points The sea surface imprint of tropical cyclone (TC) rainbands in many synthetic aperture radar images reveals their occurrence probability The rainband‐occurrence probability is overall higher in the early morning than in the evening. The feature is more obvious in strong TCs The peak region of the probability appears farther from the TC center in the evening than in the early morning

Entrainment of dry moat air with low equivalent potential temperature laterally into the eyewall and rainbands is a unique turbulent process in the inner‐core region of a tropical cyclone (TC). By analyzing in‐situ aircraft measurements collected by the reconnaissance flights that penetrated the eyewalls and rainbands of Hurricanes Rita (2005), Patricia (2015), Harvey (2017), and Michael (2018), as well as numerical simulations of Hurricanes Patricia (2015), and Michael (2018), we show that the moat air entrained into the eyewall and rainbands meets the instability criterion, and therefore, sinks unstably as a convective downdraft. The resultant positive buoyancy fluxes are an important source for the turbulent kinetic energy (TKE) in the eyewall and rainband clouds. This mechanism of TKE generation via lateral entrainment instability should be included in the TKE‐type turbulent mixing schemes for a better representation of turbulent transport processes in numerical forecasts of TCs. Plain Language Summary Turbulence is commonly regarded as a chaotic flow feature pertaining to the planetary boundary layer (PBL). In the inner core of a tropical cyclone (TC), however, turbulence can also be generated in the eyewall and rainbands above the PBL by cloud processes. The turbulence at the edge of the eyewall/rainbands not only experiences the large lateral thermodynamic contrasts across the interface between clouds and moat but also entrains moat air into clouds. Previous studies suggest that under certain conditions the entrained air into the clouds can sink unstably as convective downdrafts, leading to the generation of turbulent kinetic energy (TKE) in the clouds. By analyzing in‐situ aircraft measurements collected during the reconnaissance flights that penetrated the eyewalls and rainbands of Hurricanes Rita (2005), Patricia (2015), Harvey (2017), and Michael (2018), as well as numerical simulations of Patricia (2015) and Michael (2018), this study shows that the moat air entrained into the eyewall and rainbands meets the instability criterion. An estimate of the entrainment buoyancy fluxes suggests that the lateral entrainment instability is an important source of TKE in the eyewall and rainbands, and thus, it needs to be included in the TKE‐type turbulence schemes used in numerical forecasts of TCs. Key Points Lateral entrainment of air from the moat region into eyewall and rainbands of a tropical cyclone (TC) satisfies the instability criterion Positive buoyancy flux induced by the entrainment is an important source of turbulent kinetic energy for the eyewall and rainband clouds Lateral entrainment instability should be included in turbulent mixing parameterizations in TC forecast models

The impact of assimilating ground‐based radar reflectivity on the rainband structure and secondary eyewall formation (SEF) of Hurricane Matthew (2016) is investigated within the framework of the Hurricane Weather Research and Forecasting model and its hybrid three‐dimensional ensemble‐variational data assimilation (DA) system. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of concentric eyewall and eyewall replacement cycle. Results demonstrate that radar reflectivity DA improves the stratiform rainband analysis, resulting in the mid‐level cooling associated with mesoscale descending inflow (MDI). The MDI further contributes to the low‐level acceleration maximum with boundary layer dynamics and triggers new convective updrafts in the SEF region. Momentum budget analysis also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the SEF region in Hurricane Matthew (2016). Plain Language Summary The impact of assimilating radar reflectivity on the secondary eyewall formation (SEF) of Hurricane Matthew (2016) is investigated within the framework of the Hurricane Weather Research and Forecasting model and its hybrid three‐dimensional ensemble‐variational data assimilation (DA) system. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of secondary eyewall and eyewall replacement cycle. Results demonstrate that radar reflectivity DA improves the stratiform rainband analysis, resulting in mesoscale descending inflow (MDI). The MDI further contributes to the low‐level tangential wind increase in boundary layer and triggers new convective updrafts in the SEF region. Results also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the SEF region in Hurricane Matthew (2016). Key Points Utilizing radar reflectivity data assimilation enhances the analysis of stratiform rainbands associated with mesoscale descending inflow (MDI) Momentum budget analysis indicates that the MDI brings high angular momentum to the boundary layer through the mean vertical advection term The high angular momentum by the MDI contributes to the low‐level acceleration maximum in the secondary eyewall formation region

Using multi‐satellite observations, the cloud dynamic and microphysical characteristics were revealed during the rapid intensification (RI) of super‐typhoon Nanmadol (2022). As the storm intensifies, the eyewall contracts, the upper‐level divergence strengthens, and the cirrus cloud increases, leading to stronger upper‐level radial outflow and the vertical updraft. Meanwhile, it is found that there exists a dynamically attractive area in the outer rainbands, where particles grow effectively and form “a small amount of large particles” around 300 km from the eye. A theory of cloud dynamics‐microphysics interaction, called “tunnel theory,” is further proposed to explain the generation, accumulation, and concentrated downflow of large particles in the outer rainbands during RI. Results suggest the unique feature of particle distribution in the outer rainbands could be a potential indicator for RI. Plain Language Summary The rapid intensification (RI) of tropical cyclones (TCs) becomes more frequent in recent years, but the TC RI forecasts still remain challenging. Better understanding of the physical processes associated with RI of TCs would essentially improve its forecasting capability. The cloud dynamical and microphysical processes, especially their interactions that respond to RI are not well explored. In this study, the cloud macro and micro characteristics associated with RI of a super‐typhoon Nanmadol (2022) over the western Pacific are investigated using multiple satellites observations. The storm underwent RI during 15–16 September 2022, and it has wreaked havoc on Japan's most cities as it moved across the Japanese island afterward with a track length of about 1,120 km. It is found inside Nanmadol as well as other typhoons that a few large particles tend to occur in the outer rainbands during RI, due to the interaction of cloud dynamical and microphysical processes. Such unique feature of particle distribution in the outer rainbands could be a potential indicator for RI, and should also be paid attention to in model forecasting of typhoon precipitation. Key Points First satellite‐based observational study on the interaction of cloud dynamics and microphysics during typhoon rapid intensification (RI) The eyewall contracts, the upper‐level divergence strengthens, and the convection column increases, providing kinetic energy for typhoon RI A “tunnel theory” is proposed for the generation, accumulation, and downflow of large particles in the outer rainbands during typhoon RI

As a typhoon approaches the continent, the position where anthropogenic aerosols penetrate, the convection competition between the eyewall and peripheral rainbands, and the separate contributions of direct aerosol-radiation interactions (ARI) and indirect aerosol-cloud interactions (ACI), yield uncertainties in the convection intensification area and hence the typhoon intensity. Typhoon Lupit (2009) was simulated using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to investigate and isolate the direct and indirect effects of aerosols on the intensity, convection, and precipitation of the typhoon. Three simulations (CTL, CLEAN, and CTLARIOFF) were designed, representing a polluted case (CTL, considering the ingestion of anthropogenic aerosols with ARI and ACI), a clean maritime case (CLEAN, mainly with sea salt aerosols), and a polluted case without aerosol radiative forcing (CTLARIOFF, as per CTL but without ARI). The results showed that anthropogenic aerosols could penetrate into both the peripheral rainbands and the eyewall when the typhoon was approaching the Asian continent. Owing to the representation of the real aerosol scenario, the simulated typhoon intensity weakened and was closer to observed values in the CTL experiment. The ARI dominated over ACI with the opposite effects. Specifically, the ACI mainly enhanced the formation of ice-phase hydrometeors within the upper level of the eyewall with more freezing latent heat releases, leading to an invigoration of eyewall convection. These excess ice-phase particles melted after they descended into the warm layer below the 0°C level, which accelerated the accretion of cloud droplets by raindrops (Pcacr) and hence the mixed phase precipitation process in the eyewall. The dynamic feedback induced by the ACI enhanced the boundary layer inflow and the upper layer outflow, supporting the maintenance of strong eyewall convection and intensification of the typhoon. Inversely, the ARI heated the distant periphery low-level atmosphere at an altitude of 1–2 km by the absorbing polluted aerosols. The heated air, driven by the radial inflow, firstly went through the periphery rainbands of the typhoon and invigorated convection there due to the low-level warming. Then, the enhanced periphery convection inhibited the further transport of warm moist air into the eyewall, resulting in weakening of the eyewall convection and hence typhoon intensity. In sum, for the polluted scenario, as the typhoon approached the continent, ARI played a dominant role over ACI. The WRF-Chem model with full consideration of aerosol-cloud-radiation interactions is advantageous in terms of reliably simulating typhoon intensity and precipitation distribution.

Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r 2 value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition.

In this study, the extreme rainfall event on 2 June 2017 along the northern coast of Taiwan is studied from a modeling perspective. While a peak amount of 645 mm was observed, two 1 km experiments produced about 400 and 541 mm, respectively, using different initial and boundary conditions, and thus are compared to isolate the key reasons for a higher total amount in the second run. While the conditions in the frontal intensity and its slow movement are similar in both runs, the frontal rainband remains stationary for a long period in this second run due to a frontal disturbance that acts to enhance the prefrontal southwesterly flow and focuses its convergence with the postfrontal flow right across the coastline. Identified as the key difference, this low-pressure disturbance is supported by the observation, and without it in the first run, multiple slow-moving rainbands pass through the coastal region and produce more widely spread but less concentrated rainfall, resulting in the lower peak amount by comparison. To explore and test the effects of Taiwan's topography in this event, two additional 1 km runs are also used. It is found that the removal of the terrain in northern Taiwan allowed the postfrontal cold air to move more inland and the rainfall became less concentrated, in agreement with a recent study. Also, when the entire island topography of Taiwan is removed, the result showed significant differences. In this case, the blocking and deflecting effects on the prefrontal flow are absent, and the heavy rainfall in northern Taiwan does not occur.

The East Asian summer monsoon is unique among summer monsoon systems in its complex seasonality, exhibiting distinct intraseasonal stages. Previous studies have alluded to the downstream influence of the westerlies flowing around the Tibetan Plateau as key to its existence. We explore this hypothesis using an atmospheric general circulation model that simulates the intraseasonal stages with fidelity. Without a Tibetan Plateau, East Asia exhibits only one primary convective stage typical of other monsoons. As the plateau is introduced, the distinct rainfall stages—spring, pre-mei-yu, mei-yu, and midsummer—emerge, and rainfall becomes more intense overall. This emergence coincides with a pronounced modulation of the westerlies around the plateau and extratropical northerlies penetrating northeastern China. The northerlies meridionally constrain the moist southerly flow originating from the tropics, leading to a band of lower-tropospheric convergence and humidity front that produces the rainband. The northward migration of the westerlies away from the northern edge of the plateau leads to a weakening of the extratropical northerlies, which, coupled with stronger monsoonal southerlies, leads to the northward migration of the rainband. When the peak westerlies migrate north of the plateau during the midsummer stage, the extratropical northerlies disappear, leaving only the monsoon low-level circulation that penetrates northeastern China; the rainband disappears, leaving isolated convective rainfall over northeastern China. In short, East Asian rainfall seasonality results from the interaction of two seasonally evolving circulations—the monsoonal southerlies that strengthen and extend northward, and the midlatitude northerlies that weaken and eventually disappear—as summer progresses.